scholarly journals Targeted reduction of highly abundant transcripts with pseudo-random primers

2015 ◽  
Author(s):  
Ophélie Arnaud ◽  
Sachi Kato ◽  
Stéphane Poulain ◽  
Charles Plessy
Keyword(s):  
Gene Expression ◽  
Reverse Transcription ◽  
Flexible Tool ◽  
Expression Levels ◽  
Random Primers ◽  
Sequencing Cost ◽  
Transcriptome Diversity ◽  
Gene Expression Levels ◽  
Selection Of

Transcriptome studies based on quantitative sequencing estimate gene expression levels by measuring the abundance of target RNAs in libraries of sequence reads. The sequencing cost is proportional to the total number of sequenced reads. Therefore, in order to cover rare RNAs, considerable quantities of abundant and identical reads have to be sequenced. This major limitation can be lifted by strategies used to deplete the library from some of the most abundant sequences. However, these strategies involve either an extra handling of the input RNA sample, or the use of a large number of reverse-transcription primers (termed "not-so-random primers"), which are costly to synthetize and customize. Here, we demonstrate that with a precise selection of only 40 "pseudo-random" reverse-transcription primers, it is possible to decrease the rate of undesirable abundant sequences within a library without affecting the transcriptome diversity. "Pseudo-random" primers are simple to design, and therefore are a flexible tool for enriching transcriptome libraries in rare transcripts sequences.

scholarly journals Aromatase CYP19A1, progesterone receptor PGR and estrogen receptor ESR1 gene expression in biopsy specimens of endometrioid heterotopia and endometrial tissue by reverse transcription PCR

2019 ◽  
Vol 68 (2) ◽  
pp. 79-86
Author(s):  
Natalia Yu. Shved ◽  
Olga V. Malysheva ◽  
Natalia S. Osinovskaya ◽  
Arseniy S. Molotkov ◽  
Anna A. Tsypurdeyeva ◽  
...  
Keyword(s):  
Gene Expression ◽  
Estrogen Receptor ◽  
Progesterone Receptor ◽  
Reverse Transcription ◽  
Comparison Group ◽  
Expression Level ◽  
Tissue Samples ◽  
Expression Levels ◽  
Esr1 Gene ◽  
Gene Expression Levels

Hypothesis/aims of study. Endometriosis is one of the most pressing problems of gynecology. Clarifying the expression of the estrogen receptor (ESR1) and the progesterone receptor (PGR) genes and polymorphisms in the aromatase (CYP19A1) gene in endometriosis will expand the understanding of the pathogenesis of the disease and the causes of resistance to its therapy. The objective of this study was to conduct a comparative analysis of mRNA expression of PGR, ESR1 and CYP19A1 genes in paired samples of the eutopic endometrium and peritoneal endometrioid lesions in order to search for predictive markers of response to hormonal therapy. In the future, this may allow personalizing the selection of hormonal preparations for the treatment of endometriosis. Study design, materials and methods. Reverse transcription real-time PCR made it possible to evaluate CYP19A1, PGR and ESR1 gene expression levels in studied tissue samples from 22 patients with endometriosis and 9 women in the comparison group. Results. Quantitative analysis revealed a high heterogeneity in the expression level of the studied genes, in both the endometrium and endometrioid lesions from patients with endometriosis. In the endometrium of patients in the comparison group, the heterogeneity of the expression level was observed only for the ESR1 gene. Conclusion. Our findings suggest a high variability in CYP19A1, ESR1 and PGR gene expression levels in the endometrium and peritoneal foci in patients with endometriosis. This information indicates the need for an individual approach to prescribing targeted therapy, since it is obvious that the effect of treatment will depend primarily on the availability of a therapeutic target in a particular patient. The absence of a typical expression pattern for each of the genes in patients with endometriosis indicates the heterogeneity of the disease and the need to develop a molecular classification of this common pathology.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Sign in / Sign up

Export Citation Format

Share Document